1. Field of the Invention
The present invention relates to an apparatus and method of combining three-dimensional computer graphic simulation of volumes with computerized numerical optimization by mathematical computation.
2. Description of the Related Art Including Information Disclosed under 37 C.F.R. .sctn.1.97-1.99
Currently there are no efficient optizimation techniques or treatment planning methods for optimizing the dose delivery and shape of e.g., radiation zones in the treatment of various tumors in the body, especially the brain. Current methods involve various means of rough approximation of dose delivery to a tumor region with severe limitations on tailoring the radiation dose delivery to fit the exact dimensions and shape of the tumor volume. In addition, there is no method available which allows treatment planners to effectively minimize the dose delivery to reasonably normal brain tissue surrounding a tumor lesion and maximizing the dose to the tumor itself, other than the rather cumbersome, two-dimensional does planning systems which rely heavily on dose volume histogram calculations. As designed, currently used systems involve very tedious iterative processing by the planner to fit a radiation treatment dose and volume distribution to a given tumor volume or such similar tissue volume. Current methods are very laborious, often requiring up to a day or more to do a single dose plan or any simple iterative adjustment. Such methods are severely limited and many dose treatment planners are constrained by current technological limitations to "closely approximate" a given dose delivery. There are various devices on the market; for example, the Leksell gamma knife (see L. Leksell, Stereotaxis and Radiosurgery; An operative system, Springfield, Ill. Charles C. Thomas, 1971) and the LINAC.RTM. scalpel" (see K. R. Winston, W. Lutz, "Linear Accelerator as a Neurosurgical Tool for Stereotactic Radiosurgery," Neurosurgery, Vol. 22, No. 3, 1988; F. Colombo, A. Benedetti, et al., "External Stereotactic Irradiation by Linear Accelerator," Neurosurgery, Vol. 16, No. 2, 1985; and W. A. Friedman, F. J. Bova, "The University of Florida Radiosurgery System," Surg. Neurol., Vol 32, pp. 334-342, 1989) which are capable of deliverying the dose delivery to the tumor volume and optimizing the delivery such that dose zones can be tailored to tumor shape, configuration and size. Similarly, there are currently available methods for the accurate placement of radioisotopes, contained within catheters, in strategic areas of the brain for the direct delivery of a radiation does to a given area of the brain. This method also has similar limitations in radiation does delivery optimization (see K. Weaver, V. Smith, et al., "A CT Based Computerized Treatment Planning System for I-125 Stereotactic Brain Implants," Int. J of Radiation Oncology, Biol. Phys., Vol 18, pp. 445-454, 1990; P. J. Kelly, B. A. Kall, S. Goerss, "Computer Simulation for the Stereotactic Placement of Interstitial Radionuclide Sources into Computed Tomography-Defined Tumor Volumes," Neurosurgery, Vol. 14, No. 4, 1984; and B. Bauer-Kirpes, V. Sturm, W. Schlegel, and W. J. Lorenz, "Computerized Optimization of I-125 Implants in Brain Tumors," Int. J. Radiat. Oncol. Biol, Phys., Vol. 14 pp. 1013-1023, 1988)
None of the methods or devices described above combine three-dimensional graphics simulation and actual imaging data, such as disclosed in parent application, Ser. No. 07/500,788, with computerized numerical optimization for dose delivery treatment planning. Generally, co-pending applications Ser. No. 07/500,788 and 07/290,316, now U.S. Pat. No. 5,099,846, involve acquiring a magnetic resonance (MR), computed tomography (CT), or the like, image of a volume, such as a tumor volume, in serial sections through the volume, outlining such sections, and creating a three-dimensional simulation of the volume.
The concept of mathematical function optimization has existed since the advent of calculus and over the years has developed into very efficient and robust algorithms for constrained multidimensional nonlinear optimization. The word "optimization," when used in this context and in the specification and claim, means the rigorous use of algorithmic steps, implemented as computer code, to search for and find a mathematically defined local minimum (or maximum) or given objective function. An objective function can take many forms (e.g., calculated stress in a structural member, aerodynamic loading on a wind, or a calculated dose of brain cell irradiation), but is simply a chosen measure of the desired behavior of the object, system, or process being designed. The term "contrained optimization" then refers to the optimization process, as explained above, being conducted within certain allowable limits or constraint. For example, a desired design objective may be to design a car frame of minimum weight design. If no constraints were put on this design problem, the minimum weight design would no be able to withstand the encountered loads during operation and may not even by manufacturable. Constraints are put on the design problem that require the car frame to support certain loads under various conditions and to ensure that the final design will be manufacturable given current technology. Typically, "real world" design problems are constrained by certain necessary performance criteria. Modeling the physical behavior or "real world" objects, systems, and processes requires the use of complex nonlinear mathematical equations formed from available variable and incorporated within the computer code. Therefore, using the definitions provided in this paragraph, the term "constrained multidimensional nonlinear optimization" is defined.
Such algorithms are discussed above were designed to replace the traditional "hunt and peck" process with efficient, non-random techniques for gleaning information from the computer model in the form of slopes and curvature of the objective function "hyper-surface" (a surface with three or more variables). When coupled in this manner, the algorithms take the place of the user and autonomously search and find the optimal combination of variables to maximize or minimize a desired objective.
Two robust and efficient nonlinear optimization algorithms available in the art are the Generalized Reduced Gradient (GRG) algorithm and the Sequential Quadratic Programming (SQP) algorithm. Since the algorithms themselves are in the public domain, many private versions of these algorithms are currently available as software package.
The present invention applies these numerical optimization techniques to help improve the planning process generally for optimal dose distribution planning and specifically for the radiation treatment of brain tumors. The current techniques of radiation therapy require the designers (neurosurgeons and radiation oncologists) to perform tedious design iterations in their search for the optimal placement of radioisotope seeds and external beam trajectories. By incorporating numerical optimization algorithms into the existing framework, the operation planning process becomes virtually automatic and produces better planning designs than current techniques and in less time.